Minerva CQ provides an AI copilot that guides contact center agents in real-time during customer interactions. This system surfaces personalized context, suggests optimal adaptive workflows, and provides dialogue assistance to drive efficient resolution. The platform aims to improve customer experience, reduce handle times, and elevate agent performance through immediate, context-aware support.
Funding
$4.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Customer service agents often struggle to efficiently navigate complex systems and access relevant information during interactions, leading to longer resolution times and decreased customer satisfaction. Agents may need to consult multiple systems and knowledge bases, hindering their ability to provide quick and accurate responses.
Solution
Minerva CQ provides an AI-powered copilot that integrates directly into the agent's workflow, offering real-time contextual insights and adaptive workflows. By analyzing conversation data, the system surfaces relevant knowledge, suggests optimal dialogue, and provides behavioral cues to guide agents toward effective resolutions. The AI copilot streamlines the agent experience by consolidating information from multiple sources into a single screen, reducing the need to switch between applications. Minerva CQ enhances agent performance, reduces handle times, and improves overall customer experience through AI-driven support.
Target Audience
Minerva CQ targets contact centers and customer service organizations seeking to improve agent efficiency, reduce resolution times, and enhance customer satisfaction.
Features
- Real-time analysis of customer-agent interactions to provide contextual insights
- Adaptive workflows that adjust dynamically based on the conversation's progress
- Dialogue suggestions to guide agents on what to say
- Sentiment detection to provide behavioral cues
- AI-powered call summarization to reduce post-call work
- RAG (Retrieval-Augmented Generation) powered knowledge surfacing
- Single-screen UI that simplifies the agent experience
- Integration with existing enterprise systems and machine learning models
- Omnichannel data parsing from multiple sources